Artificial intelligence (AI) allows to learn and expand targeted regions of chemical space defined by collections of examples, and is revolutionizing molecular design. AI methods were mostly designed for text data and are therefore particularly well suited for peptide design because peptides can be simply written as character strings, each letter representing a different amino acid. Here we present three recent examples from our laboratory aimed at a selected expansion of the peptide chemical space, focusing on antimicrobial activities. Two examples involve molecular fingerprint guided selection in targeted libraries generated either by enumeration or using a genetic algorithm, and a third example is based on Recurrent Neural Networks for both sequence enumeration and compound selection. We will compare and discuss the different approaches in terms of scope, achievable molecular diversity, and results.

Targeted expansion of the peptide chemical space by enumeration, genetic algorithms, and machine learning

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.